1. Field of the Invention
The present invention relates to an automatic brake control system and a method thereof and more particularly to an automatic brake control system having a learning capability employing neural networks and the method thereof.
2. Prior Arts
As an example of known automatic brake control systems, Japanese Unexamined Patent Application Toku-Kai-Hei 5-305839 discloses a technique in which engine power and brake force of a vehicle are automatically controlled based on its vehicle speed, acceleration, deceleration and distances between the vehicle and another vehicle running ahead. Further, as an example of technique introducing a neural network into automobiles, Japanese Unexamined Patent Application Toku-Kai-Hei 4-138970 proposes a rear wheel steering system in which a yaw rate and other control parameters are estimated by a neural network learned by the back-propagation method and a target steering angle of the rear wheel is determined based on these estimated control parameters. Furthermore, Japanese Unexamined Patent Application Toku-Kai-Hei 6-286630 discloses a technique in which a road friction coefficient is estimated from a vehicle speed and other parameters by employing the neural network.
It can be considered that the neural network is applied to the estimation of control parameters of the vehicle for determining a target amount of brake control. However, it is insufficient to apply the neural network only for estimating the control parameters of the vehicle. The automatic brake control system is dependent on not only the vehicle characteristics but also human factors such as a driver's driving habits. More specifically, for example, the timing or the way of pressing a brake pedal differs from person to person. Some drivers press the brake pedal rather early but the way of depression is moderate and some drivers press the brake pedal rather late but the way of depression is aggressive.
Consequently, it is impossible to realize an automatic brake control system which is preferable to every driver as far as a target brake control amount is determined based on the vehicle parameters only. The automatic brake control is operated too slowly for a driver having a habit of applying brake rather early and it is operated too fast for a driver having a habit of applying brake rather late. Further, since some drivers may feel awkwardness depending upon the transient condition of the vehicle during pressing of the brake pedal, only correcting parameters of a given calculation formula brings an insufficient result.
Therefore, it is more important to apply the neural network directly to the calculation itself of the target brake control amount than to the estimation of vehicle parameters. Namely, the automatic brake control can be provided with a greater flexibility and a higher adaptability by introducing a learning function into calculating means for calculating the target brake control amount.
However, in applying the neural network to the calculation of the target brake control amount, using a small size of network for the calculation without taking any measures leads to an insufficient accuracy of the result of the calculation. Further, there are difficulties in ensuring the compatibility of the control with the learning or in how to select learning contents properly.